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CS 277: Data Mining Mining Web Link Structure

CS 277: Data Mining Mining Web Link Structure. Class Presentations. In-class, Tuesday and Thursday next week 2-person teams: 6 minutes, up to 6 slides, 3 minutes/slides each person 1-person teams 4 minutes, up to 4 slides Powerpoint or PDF is fine

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CS 277: Data Mining Mining Web Link Structure

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  1. CS 277: Data MiningMining Web Link Structure

  2. Class Presentations • In-class, Tuesday and Thursday next week • 2-person teams: • 6 minutes, up to 6 slides, 3 minutes/slides each person • 1-person teams • 4 minutes, up to 4 slides • Powerpoint or PDF is fine • Needs to be emailed by 12 noon on the day of presentation • Order of presentations will be announced later in the week

  3. Web Mining • Web = a potentially enormous “data set” for data mining • 3 primary aspects of “Web mining” • Web page content e.g., clustering Web pages based on their text content • Web connectivity e.g., characterizing distributions on path lengths between pages e.g., determining importance of pages from graph structure • Web usage e.g., understanding user behavior from Web logs • All 3 are interconnected/interdependent • E.g., Google (and most search engines) use both content and connectivity • These slides: Web connectivity

  4. The Web Graph • G = (V, E) • V = set of all Web pages • E = set of all hyperlinks • Number of nodes ? • Difficult to estimate • Crawling the Web is highly non-trivial > 10 billion • Number of edges? • E = O(|V|) • i.e., mean number of outlinks per page is a small constant

  5. The Web Graph • The Web graph is inherently dynamic • nodes and edges are continually appearing and disappearing • Interested in general properties of the Web graph • What is the distribution of the number of in-links and out-links? • What is the distribution of number of pages per site? • Typically power-laws for many of these distributions • How far apart are 2 randomly selected pages on the Web? • What is the “average distance” between 2 random pages? • And so on…

  6. Social Networks • Social networks = graphs • V = set of “actors” (e.g., students in a class) • E = set of interactions (e.g., collaborations) • Typically small graphs, e.g., |V| = 10 or 50 • Long history of social network analysis (e.g. at UCI) • Quantitative data analysis techniques that can automatically extract “structure” or information from graphs • E.g., who is the most important “actor” in a network? • E.g., are there clusters in the network? • Comprehensive reference: • S. Wasserman and K. Faust, Social Network Analysis, Cambridge University Press, 1994.

  7. Node Importance in Social Networks • General idea is that some nodes are more important than others in terms of the structure of the graph • In a directed graph, “in-degree” may be a useful indicator of importance • e.g., for a citation network among authors (or papers) • in-degree is the number of citations => “importance” • However: • “in-degree” is only a first-order measure in that it implicitly assumes that all edges are of equal importance

  8. Recursive Notions of Node Importance • wij = weight of link from node i to node j • assume Sjwij= 1 and weights are non-negative • e.g., default choice: wij= 1/outdegree(i) • more outlinks => less importance attached to each • Define rj = importance of node j in a directed graph rj = Siwijrii,j = 1,….n • Importance of a node is a weighted sum of the importance of nodes that point to it • Makes intuitive sense • Leads to a set of recursive linear equations

  9. Simple Example 1 2 3 4

  10. Simple Example 1 1 2 3 0.5 0.5 0.5 0.5 0.5 0.5 4

  11. Simple Example 1 1 2 3 0.5 0.5 0.5 0.5 0.5 Weight matrix W 0.5 4

  12. Matrix-Vector form • Recall rj = importance of node j rj = Si wij rii,j = 1,….n e.g., r2 = 1 r1 + 0 r2 + 0.5 r3 + 0.5 r4 = dot product of r vector with column 2 of W Let r = n x 1 vector of importance values for the n nodes Let W = n x n matrix of link weights => we can rewrite the importance equations as r = WTr

  13. Eigenvector Formulation Need to solve the importance equations for unknown r, with known W r = WTr We recognize this as a standard eigenvalue problem, i.e., A r = lr (where A = WT) with l = an eigenvalue = 1 and r = the eigenvector corresponding to l = 1

  14. Eigenvector Formulation Need to solve for r in (WT – l I) r = 0 Note: W is a stochastic matrix, i.e., rows are non-negative and sum to 1 Results from linear algebra tell us that: (a) Since W is a stochastic matrix, W and WT have the same eigenvectors/eigenvalues (b) The largest of these eigenvalueslis always 1 (c) the vector rcorresponds to the eigenvector corresponding to the largest eigenvector of W (or WT)

  15. Solution for the Simple Example Solving for the eigenvector of W we get r = [0.2 0.4 0.133 0.2667] Results are quite intuitive, e.g., 2 is “most important” 1 1 2 3 0.5 0.5 W 0.5 0.5 0.5 0.5 4

  16. PageRank Algorithm: Applying this idea to the Web • Crawl the Web to get nodes (pages) and links (hyperlinks) [highly non-trivial problem!] • Weights from each page = 1/(# of outlinks) • Solve for the eigenvector r (for l= 1) of the weight matrix Computational Problem: • Solving an eigenvector equation scales as O(n3) • For the entire Web graph n > 10 billion (!!) • So direct solution is not feasible Can use the power method (iterative)r(k+1) = WTr (k) for k=1,2,…..

  17. Power Method for solving for r r(k+1) = WTr(k) Define a suitable starting vector r(1) e.g., all entries 1/n, or all entries = indegree(node)/|E|, etc Each iteration is matrix-vector multiplication =>O(n2) - problematic? no: since W is highly sparse (Web pages have limited outdegree), each iteration is effectively O(n) For sparse W, the iterations typically converge quite quickly: - rate of convergence depends on the “spectral gap” -> how quickly does error(k) = (l2/ l1)k go to 0 as a function of k ? -> if |l2| is close to 1 (= l1) then convergence is slow - empirically: Web graph with 300 million pages -> 50 iterations to convergence (Brin and Page, 1998)

  18. Basic Principles of Markov Chains Discrete-time finite-state first-order Markov chain, K states Transition matrix A = K x K matrix • Entry aij = P( statet = j | statet-1 = i), i, j = 1, … K • Rows sum to 1 (since SjP( statet = j | statet-1 = i) = 1) • Note that P(state | ..) only depends on statet-1 P0 = initial state probability = P(state0 = i), i = 1, …K

  19. Simple Example of a Markov Chain 0.8 0.9 1 0.2 2 0.1 0.2 0.2 3 0.6 K = 3 A = P0 = [1/3 1/3 1/3]

  20. Steady-State (Equilibrium) Distribution for a Markov Chain Irreducibility: • A Markov chain is irreducible if there is a directed path from any node to any other node Steady-state distribution p for an irreducible Markov chain*: pi = probability that in the long run, chain is in state I The p’s are solutions to p = Atp Note that this is exactly the same as our earlier recursive equations for node importance in a graph! *Note: technically, for a meaningful solution to exist for p, A must be both irreducible and aperiodic

  21. Markov Chain Interpretation of PageRank • W is a stochastic matrix (rows sum to 1) by definition • can interpret W as defining the transition probabilities in a Markov chain • wij= probability of transitioning from node i to node j • Markov chain interpretation:r = WTr -> these are the solutions of the steady-state probabilities for a Markov chain page importance  steady-state Markov probabilities  eigenvector

  22. The Random Surfer Interpretation • Recall that for the Web model, we set wij= 1/outdegree(i) • Thus, in using W for computing importance of Web pages, this is equivalent to a model where: • We have a random surfer who surfs the Web for an infinitely long time • At each page the surfer randomly selects an outlink to the next page • “importance” of a page = fraction of visits the surfer makes to that page • this is intuitive: pages that have better connectivity will be visited more often

  23. Potential Problems 1 2 3 Page 1 is a “sink” (no outlink) Pages 3 and 4 are also “sinks” (no outlink from the system) Markov chain theory tells us that no steady-state solution exists - depending on where you start you will end up at 1 or {3, 4} Markov chain is “reducible” 4

  24. Making the Web Graph Irreducible • One simple solution to our problem is to modify the Markov chain: • With probability a the random surfer jumps to any random page in the system (with probability of 1/n, conditioned on such a jump) • With probability 1-a the random surfer selects an outlink (randomly from the set of available outlinks) • The resulting transition graph is fully connected => Markov system is irreducible => steady-state solutions exist • Typically a is chosen to be between 0.1 and 0.2 in practice • But now the graph is dense! However, power iterations can be written as:r(k+1) = (1- a) WTr(k) + (a/n) 1T • Complexity is still O(n) per iteration for sparse W

  25. The PageRank Algorithm • S. Brin and L. Page, The anatomy of a large-scale hypertextual search engine, in Proceedings of the 7th WWW Conference, 1998. • PageRank = the method on the previous slide, applied to the entire Web graph • Crawl the Web • Store both connectivity and content • Calculate (off-line) the “pagerank” r for each Web page using the power iteration method • How can this be used to answer Web queries: • Terms in the search query are used to limit the set of pages of possible interest • Pages are then ordered for the user via precomputedpageranks • The Google search engine combines r with text-based measures • This was the first demonstration that link information could be used for content-based search on the Web

  26. Link Structure helps in Web Search Singhal and Kaszkiel, WWW Conference, 2001 SE1, etc, indicate different (anonymized) commercial search engines, all using link structure (e.g., PageRank) in their rankings TFIDF is a state-of-the-art search method (at the time) that does not use any link structure

  27. Ranking of pages more important than exact values of pi Pre-compute and store the PageRank of each page. PageRank independent of any query or textual content. Ranking scheme combines PageRank with textual match Unpublished Many empirical parameters and human effort Criticism : Ad-hoc coupling of query relevance and graph importance Massive engineering effort Continually crawling the Web and updating page ranks PageRank architecture at Google

  28. Link Manipulation

  29. Conclusions • PageRank algorithm was the first algorithm for link-based search • Many extensions and improvements since then • See papers on class Web page • Same idea used in social networks for determining importance • Real-world search involves many other aspects besides PageRank • E.g., use of logistic regression for ranking • Learns how to predict relevance of page (represented by bag of words) relative to a query, using historical click data • See paper by Joachims on class Web page • Additional slides (optional) • HITS algorithm, Kleinberg, 1998

  30. Additional optional Slides

  31. PageRank: Limitations • “rich get richer” syndrome • not as “democratic” as originally (nobly) claimed • certainly not 1 vote per “WWW citizen” • also: crawling frequency tends to be based on pagerank • for detailed grumblings, see www.google-watch.org, etc. • not query-sensitive • random walk same regardless of query topic • whereas real random surfer has some topic interests • non-uniform jumping vector needed • would enable personalization (but requires faster eigenvector convergence) • Topic of ongoing research • ad hoc mix of PageRank & keyword match score • done in two steps for efficiency, not quality motivations

  32. J. Kleinberg, Authorative sources in a hyperlinked environment, Proceedings of ACM SODA Conference, 1998. HITS – Hypertext Induced Topic Selection Every page u has two distinct measures of merit, its hub score h[u] and its authority score a[u]. Recursive quantitative definitions of hub and authority scores Relies on query-time processing To select base set Vq of links for query q constructed by selecting a sub-graph R from the Web (root set) relevant to the query selecting any node u which neighbors any r \in R via an inbound or outbound edge (expanded set) To deduce hubs and authorities that exist in a sub-graph of the Web HITS: Hub and Authority Rankings

  33. Authority and Hubness 5 2 3 1 1 6 4 7 h(1) = a(5) + a(6) + a(7) a(1) = h(2) + h(3) + h(4)

  34. Authority and Hubness Convergence • Recursive dependency: • a(v)  Σ h(w) • h(v)  Σ a(w) w Є pa[v] w Є ch[v] • Using Linear Algebra, we can prove: a(v) and h(v) converge

  35. HITS Example Find a base subgraph: • Start with a root set R {1, 2, 3, 4} • {1, 2, 3, 4} - nodes relevant to the topic • Expand the root set R to include all the children and a fixed number of parents of nodes in R  A new set S (base subgraph) 

  36. HITS Example Results Authority Hubness 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Authority and hubness weights

  37. Stability of HITS vs PageRank (5 trials) HITS randomly deleted 30% of papers PageRank

  38. HITS vs PageRank: Stability • e.g. [Ng & Zheng & Jordan, IJCAI-01 & SIGIR-01] • HITS can be very sensitive to change in small fraction of nodes/edges in link structure • PageRank much more stable, due to random jumps • propose HITS as bidirectional random walk • with probability d, randomly (p=1/n) jump to a node • with probability d-1: • odd timestep: take random outlink from current node • even timestep: go backward on random inlink of node • this HITS variant seems much more stable as d increased • issue: tuning d (d=1 most stable but useless for ranking)

  39. Recommended Books http://www.cs.berkeley.edu/~soumen/mining-the-web/ http://www.oreilly.com/catalog/googlehks/ http://www.google.com/apis/

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